Some studies [1]}, [2]}, [3]}, [4]}, [5]} go further than simply using attribute: they point out that attributes are not isolated but connected, which forms knowledge graph (KG). Generally, KG is a heterogeneous graph, where nodes correspond to entities (item or attribute of item), and edges correspond to relationships. Compared with KG-free methods, combining KG into recommendation has three benefits [2]}: (1) The rich semantic relevance between items in KG helps to explore their potential connections and improve the accuracy of results; (2) The various types of relationships in a KG help to reasonably expand users' interests and increase the diversity of recommended items; (3) KG connects the items that users likes and the items recommended to users, which brings interpretability to recommendation systems.
